Unsupervised Learning Why I Think Karpathy is Wrong on the AGI Timeline
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Oct 20, 2025 The discussion kicks off with a critique of Karpathy's AGI timeline claims. Definitions matter: Karpathy sees AGI as achieving human-level economic value, but there’s a better benchmark. The conversation dives into the layers beyond simple language models, showcasing how systems can vastly enhance capabilities. Insights on potential job displacement reveal a concerning reality for knowledge workers. Practical strategies for overcoming LLM limits are also explored, with predictions suggesting AGI could emerge before 2030.
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Definition Shapes AGI Predictions
- Karpathy's AGI definition focuses on doing any economically valuable work as well as humans.
- Daniel prefers a practical definition centered on AI systems replacing average knowledge workers.
Systems, Not Naked Models
- Daniel emphasizes that real products are AI systems, not just base LLMs.
- Systems combine models with engineering scaffolding to produce the user-facing capability.
Composite Systems Close Capability Gaps
- Companies will stitch components like RAG, context windows, and skills to overcome LLM limits.
- These composite systems will reach 'good enough' generality for many jobs before perfect models exist.